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Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2.9 KiB
2.9 KiB
In [1]:
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChainIn [2]:
from langchain.prompts import PromptTemplate
template = """Between >>> and <<< are the raw search result text from google.
Extract the answer to the question '{query}' or say "not found" if the information is not contained.
Use the format
Extracted:<answer or "not found">
>>> {requests_result} <<<
Extracted:"""
PROMPT = PromptTemplate(
input_variables=["query", "requests_result"],
template=template,
)In [3]:
chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))In [4]:
question = "What are the Three (3) biggest countries, and their respective sizes?"
inputs = {
"query": question,
"url": "https://www.google.com/search?q=" + question.replace(" ", "+")
}In [5]:
chain(inputs)Out [5]:
{'query': 'What are the Three (3) biggest countries, and their respective sizes?',
'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',
'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}In [ ]: